We miss in person relationships and live experiences that are not available with virtual and hybrid meetings. Virtual platforms have their issues which makes us desire more for in person gatherings. We’re still not back to normal but we are easing our way back. Hope you feel the same and will join us at these upcoming events to share and engage in a valuable discussion.
Upcoming Events in 2022
CPAC, Seattle, WA, May 2-3
Routine Quality Assessment – Similarities and uniqueness of machine learning and chemometrics and how they combine to form robust solutions.
PEFTEC, Rotterdam, June 8-9
Streamlining the Use of Chemometrics – Faster response, improved flow of information and a significant process understanding nearly cost-free.
IFPAC, Bethesda, MD, June 12-15
Agile Process Analytics – Combining technical tools to augment or replace tasks that consume brainpower for timely response and greater profits with future goals of optimization with automated spectroscopy calibration.
SciX, Kentucky, Oct 2-7
Optimizing Spectroscopy Performance – Lessening the workload with automation of models and maintaining them quickly and easily for robust, reliable, and timely calibrations.
Brian Rohrback, president of Infometrix, will join Oliver Steinhof, PAT Scientist at Biogen and Nicolas Langenegger, Senior Associate Scientist at Biogen for this free webinar on September 20, 2021 at 10:00am EST.
Register at: biopharma-asia.com
The increasing use of multivariate models both as part of the control strategy in commercial (bio)pharmaceutical production as well as for process monitoring calls for an efficient strategy for model development and model life cycle management. The traditional approach to develop multivariate models based on spectroscopy involves manual data management such as selection and transfer of spectroscopic data, import into modeling software and selection/exclusion of data. That is followed by addition of reference data, alignment of time stamps and import into the modeling software. 90% of the time required to construct a multivariate model is spent on data preparation. It was decided to develop a solution to automate these steps to prepare (stage) the data required for model development, reducing the time required to prepare a typical set of batch data to about five minutes. A second tool was developed to automatically optimize data pretreatment parameters and spectral range for PLS models. Both tools allow our scientists to invest their time into more value-added activities.
Join Brian Rohrback at the 2021 ISA Analysis Division Virtual Conference
March 23, 2021 at 12:00 ET
Register and be ready to take part in these in-depth discussions at www.isa.org/ad
Rethinking Calibration for Process Spectrometers
The talk focuses on a generic, machine-learning approach that addressed the primary bottlenecks of mustering data, automating analyzer calibration, and tracking data and model performance over time. The gain in efficiency has been considerable, and the fact that the approach does not disturb any of the legacy (i.e., no changes or alterations to any analyzer or software in place) made deployment simple. The result is a standardized procedure for doing calibrations that adheres to best practices, archives all data and models with easy access in mind, and delivers models in any format.
Join Brian Rohrback, President of Infometrix on Feb. 25th at 1:00pm ET.
Free Webinar: Process Control & Instrumentation Series
Practical AI: In Search of Dynamic, Autonomous Process Analytics
The application of the concepts behind artificial intelligence and machine learning mandates a systematic approach to extracting information from multiple, byte-dense data sources. Effective extraction of this information leads to improvements in decision making at all levels of the chemical, petrochemical, and petroleum industries. To accomplish anything in the AI space, we need to combine traditional approaches in statistics, database organization, pattern recognition, and chemometrics with some newer concepts tied to better understanding of data mining, neurocomputing, and machine learning. This is an introduction to a practical approach to deploying AI and how a multi-company, multi-industry, hydrocarbon processing consortium, established eight years ago to re-evaluate how the calibration process for sensors and analyzers could be managed more efficiently. The focus spans optical spectrometers, chromatographs, and process sensors, independently and in combination, with a shift from current practices to approaches that take advantage of the computational power at our fingertips.
Dr. Rohrback’s expertise covers the integration of multivariate data processing for process analyzers and laboratory instruments catering to routine quality analysis. Prior to his current position, he worked for Cities Services Oil Company, now Occidental Petroleum, with industry positions including research scientist managing the chromatography group, an exploration geologist, and manager of planning/budget for EAME. He holds a B.S. in chemistry, a Ph.D. in organic geochemistry, and an MBA. His 50-year span of published works include topics in petroleum exploration, chemical plant optimization, clinical and pharmaceutical diagnostics, informatics, pattern recognition and multivariate analysis.
2020 AIChE Spring Meeting and 16th Global Congress on Process Safety
Aug 19, 2020
See abstract below for presentation at the 2020 AIChE Spring Meeting. Join us or contact us for more information.
Harnessing Big Data Approaches and AI in the Chemical Processing Industry
The term Big Data implies a systematic approach to extracting information from multiple, byte-dense data sources. Effective extraction of this information leads to improvements in decision making at all levels of the chemical, petrochemical, and petroleum industries. To accomplish anything in the Big Data space, we need to combine traditional approaches in statistics, database organization, pattern recognition, and chemometrics with some newer concepts tied to better understanding of data mining, neuro-computing, and machine learning. In order for industry to achieve the goals that this form of AI promises, we need to approach the issues with more than just words.
This is a summary of a multi-company, multi-industry, hydrocarbon processing consortium, established seven years ago to re-evaluate how the calibration process for sensors and analyzers could be managed more efficiently. The focus spans optical spectrometers, chromatographs, and process sensors, independently and in combination. The idea is to enable a shift from current practices to approaches that take advantage of the computational power at our fingertips. It was critical to prioritize solutions that are non-disruptive, utilize legacy systems, and lessen the workload rather than layer on additional requirements. The result is a choice of tools available to consume the data and generate actionable, process-specific information are in hand. The analyzers in place, optical spectrometers in particular, represent the low-hanging fruit.